Towards a Low Power Hardware Accelerator for Deep Neural Networks

In this project, we take a first step towards building a low power hardware accelerator for deep learning. We focus on RBM based pretraing of deep neural networks and show that there is significant robustness to random errors in the pre-training, training and testing phase of using such neural networks. We propose to leverage such robustness to build accelerators using low power but possibly unrelaible hardware substrate.